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AI/ML with FEA and Data Analytics Expertise

1 month ago

Champaign, United States Cynet Systems Full time
Job Description:

Pay Range: $95000year - $100000year

Requirement:
  • Develop AI/ML and Gen AI Models: Design, train, and implement machine learning and Gen AI models to drive efficiencies in structural analysis, and simulation processes.
  • Integrate Gen AI for Automation: Leverage Generative AI to automate complex engineering workflows, optimize design iterations, and enhance predictive accuracy in simulations.
  • FEA Validation and Analysis: Use FEA tools (ANSYS, Hypermesh) to validate AI/ML and Gen AI predictions, ensuring robust and reliable structural performance assessments.
  • Data Analytics and Pipeline Development: Build data pipelines to process large engineering datasets and extract actionable insights, enabling the improvement and validation of AI/ML models.
  • Cross-Functional Collaboration: Work closely with engineering, R&D, and data science teams to integrate AI/ML and Gen AI solutions into existing engineering workflows.
  • Documentation and Reporting: Prepare comprehensive reports and documentation on AI/ML model performance, methodology, and implementation outcomes for stakeholder review.
  • Stay Updated with Industry Trends: Keep abreast of advancements in AI, ML, and Gen AI relevant to engineering, introducing new tools, frameworks, and methodologies to the team.
Qualifications:
  • Bachelor's or Master's degree in Mechanical Engineering.
Experience:
  • 5+ years in stress analysis of heavy engineering with working knowledge on AI/ML, Gen AI, data analytics
Technical Skills:
  • Proficiency in FEA tools (ANSYS ABAQUS, Optistruct, Hypermesh) with a focus on structural and predictive analysis.
  • Strong programming skills in Python, R, or MATLAB, with experience in building AI/ML models and working with Gen AI frameworks.
  • Experience with machine learning libraries (e.g., TensorFlow, PyTorch, Scikit-learn) and Generative AI platforms (e.g., OpenAI, Hugging Face).
  • Strong skills in data preprocessing, data analytics, and statistical techniques for engineering applications.
Soft Skills:
  • Excellent problem-solving and analytical skills, with the ability to communicate complex AI/ML and engineering concepts effectively to diverse stakeholders.